Introduction to ggplot

Example Code and Applications

Why ggplot

• Consists of an underlying grammar of graphics that allows for easy, structured, logical programming.
• Flexible, allows you to customize almost all aspects of the plot.
• Built in themes that polish the plot’s appearance, and makes it more “publication ready.”
• Open-sourced and collaborative, so there a number of packages written that extend ggplot to even further capabilities.

1. Installation and Set-Up

#install.packages("tidyverse") #only need to run once
library(tidyverse) 

## Registered S3 methods overwritten by 'tibble':
##   method     from  
##   format.tbl pillar
##   print.tbl  pillar

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──

## ✓ ggplot2 3.3.5     ✓ purrr   0.3.4
## ✓ tibble  3.0.3     ✓ dplyr   1.0.7
## ✓ tidyr   1.1.0     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

2. Working Example Data

This exercise will use the storms data set that comes with dplyr. (n=10,010)

head(storms)

## # A tibble: 6 x 13
##   name   year month   day  hour   lat  long status category  wind pressure
##   <chr> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <chr>  <ord>    <int>    <int>
## 1 Amy    1975     6    27     0  27.5 -79   tropi… -1          25     1013
## 2 Amy    1975     6    27     6  28.5 -79   tropi… -1          25     1013
## 3 Amy    1975     6    27    12  29.5 -79   tropi… -1          25     1013
## 4 Amy    1975     6    27    18  30.5 -79   tropi… -1          25     1013
## 5 Amy    1975     6    28     0  31.5 -78.8 tropi… -1          25     1012
## 6 Amy    1975     6    28     6  32.4 -78.7 tropi… -1          25     1012
## # … with 2 more variables: ts_diameter <dbl>, hu_diameter <dbl>

names(storms)

##  [1] "name"        "year"        "month"       "day"         "hour"       
##  [6] "lat"         "long"        "status"      "category"    "wind"       
## [11] "pressure"    "ts_diameter" "hu_diameter"

3. ggplot basics

• Overarching Idea: Using ggplot, we can specify different parts of the plot and combine them together using the “+” operator.

• ggplot Foundation:

  • aes: (aesthetics), a mapping between a visual cue and a variable. Examples include:
    • position (i.e., on the x and y axes)
    • color (“outside” color)
    • fill (“inside” color)
    • shape (of points)
      
    • line type
      
    • size
  • geoms: (geometric objects), the actual marks we put on a plot. Examples include:
    • points (geom_point)
    • lines (geom_line)
    • boxplot (geom_boxplot).

Example Plot 1

This is a simple example using just the foundational ggplot plot elements, aesthetics and geoms. Here, using the storms data, we have mapped ts_diameter (wind diameter) to our x variable and pressure (air pressure) to our y variable. We are using geom_point() for a scatter plot.

ex_plot1 <- ggplot(data = storms, aes(x = ts_diameter, y = pressure)) +
  geom_point()

ex_plot1

## Warning: Removed 6528 rows containing missing values (geom_point).

Note: the “warning” indicates that there were some NAs in the variables we were plotting

4. Scales

• Aesthetic mapping only says that a variable should be mapped to an aesthetic. It doesn’t say how that should happen.
  • Ex. when mapping a variable to color with aes(color = var3), you don’t say what color scheme should be used.
• Describing what colors/shapes/sizes etc. to use is done by modifying the corresponding scale - In ggplot2, scales include:
  • Position
  • color, fill, and alpha
  • Size
  • Shape
  • Linetype
• Some scales can be changed within a geom() call, for example:
  • alpha: opacity of a geom with values ranging from 0 to 1. Lower values correspond to more transparent colors.
    
  • size: width in mm

Example Plot 2

In this first scale example plot, we examine some scales that can be changed from within a geom() call. Here, we first map the color of the points to the storm category variable, then we modify some scale attributes by changing the alpha to make the points more transparent, and decreasing the size of the points.

Of note, when you map a variable to an aesthetic, as we did here with color, ggplot automatically generates a legend for you and uses a default color scheme. A separate scale function, like I mentioned previously (and seen in the next example), is needed to customize the colors for the scale.

ex_plot2 <- ggplot(data = storms, aes(x = ts_diameter, y = pressure)) +
  geom_point(aes(color = category), #also in this ex., we map color to storm category
             alpha = 0.3,
             size = 1.5)
ex_plot2

Example Plot 3

Other scales need to be changed in their own statement, following the scale_<aesthetic>_<type> naming scheme. For Example:

• specifying specific color scales
• changing number breaks/increments
• changing the specific shapes mapped to a variable

In this example plot, I use a neat color scale resource, the RColorBrewer package. The package includes color scales that use hand selected colors that are designed to work well in a wide variety of data situations. Also, a really nice feature of this package is that they have created several “color-blind friendly” schemes.

Note: In the next example, I use the YlGn scheme.

#install.packages("RColorBrewer") #only need to run once
library(RColorBrewer)

ex_plot3 <- ggplot(data = storms, aes(x = ts_diameter, y = pressure)) +
  geom_point(aes(color = category), 
             alpha = 0.3,
             size = 1.5) +
  scale_colour_brewer(palette = "YlGn")
ex_plot3

Custom Color Scales

In addition to pre-existing color schemes, you can also generate custom color palettes using scale_fill_manual and scale_color_manual. Both of these functions accept actual color names like “red” or “blue,” or you can manually enter hex values for more intricate colors. I use a google chrome eye dropper extension, linked here.

Example Plot 4

In the plot I just created, the yellow and light green colors are kind of hard to see, so I may want to change that. For example, I can change the color scheme of our plot to match the a storm weather radar color scheme, since this is storm data, after all(!). In this example plot, I do that using the google chrome eye dropper and scale_color_manual function.

ex_plot4 <- ggplot(data = storms, aes(x = ts_diameter, y = pressure)) +
  geom_point(aes(color = category), 
             alpha = 0.3,
             size = 1.5) +
  scale_color_manual(values=c("#00b10d", "#ffd800", "#ff9901", "#ff0100", "#be0032",
                    "#79016d", "#7a2fa1")) #pass in color codes

ex_plot4

Note: - Order of colors codes passed in matches the order of the mapped variables:

• Category -1 = #00b10d
• Category 0 = #ffd800
• Etc.

For continuous variables, you can add a breaks=c() to indicate where you want the color change to occur:

• Ex: scale_color_manual(values = c(“red”, “blue”, “green”), breaks = c(0, 5, 10))

5. Titles, subtitles, & labels: Basics

Main functions for adding titles, subtitles & labels:

• Labs(x = , y = , title = , subtitle = , caption = )
• ggtitle(title = , subtitle = )
• scale_y/x_continuous/discrete(name =)

Titles and Subtitles:

• ggtitle() function
• labs() function

Labels:

• labs() function
• scale_x_continuous()
• scale_x_discrete()
• scale_y_continuous()
• scale_y_discrete()

Example Plot 5

In this example, I added the labs() function to my plot for all plot titles, subtitles, captions and axes labels.

ex_plot5 <- ggplot(data = storms, aes(x = ts_diameter, y = pressure)) +
  geom_point(aes(color = category), 
             alpha = 0.3,
             size = 1.5) +
  scale_color_manual(values=c("#00b10d", "#ffd800", "#ff9901", "#ff0100", "#be0032",
                    "#79016d", "#7a2fa1")) +
  labs(x = "wind diameter", y = "air pressure", title = "Example Plot 5", 
       subtitle = "Plot of air pressure by wind diameter", caption = "Data Source: Dplyr Storms") 

ex_plot5

Alternative coding options: - ggtitle(title = "Example Plot 5", subtitle = "Plot of air pressure by wind diameter") - scale_y_continuous(name = "air pressure") + scale_x_continuous(name = "wind diameter)

6. Titles, subtitles & labels: Extensions

You can also use theme() to change the face, font, and size of a title, subtitle or label, as seen in Example Plot 6.

• Ex. theme(plot.title = element_text(color = “”, size = , face = “”)

Example Plot 6

In this example, using one theme function, I changed the face of the title to bold, the face of the subtitle to italics, and decreased the size of the caption to 6pt.

ex_plot6 <- ggplot(data = storms, aes(x = ts_diameter, y = pressure)) +
  geom_point(aes(color = category), 
             alpha = 0.3,
             size = 1.5) +
  scale_color_manual(values=c("#00b10d", "#ffd800", "#ff9901", "#ff0100", "#be0032",
                    "#79016d", "#7a2fa1")) +
  labs(x = "wind diameter", y = "air pressure", title = "Example Plot 6", 
       subtitle = "Plot of air pressure by wind diameter", caption = "Data Source: Dplyr Storms") +
  theme(plot.title = element_text(face = "bold"),
        plot.subtitle = element_text(face = "italic"),
        plot.caption = element_text(size = 6))

ex_plot6

7. Legends

Our plot currently has the default legend that ggplot generates any time you map an aesthetic to a variable. We can also customize a legend using separate ggplot functions.

• Changing Legend title:
  • From within the lab() function, specify color = “legend name” (or fill = , shape = , etc.)
• Changing Legend Labels:
  • Use the respective scale() function scale_color_manual(), scale_color_discrete(), scale_color_continuous() etc.
• Changing Legend Background Color:
  • From within the theme() function, use legend.background = element_rect(fill = ”<color>")
• Changing Legend Position:
  • From within the theme() function, use legend.position = “top/bottom/bottom left”

This is a far from exhaustive list of legend capabilities.

Example Plot 7

In this example, I manipulated 5 aspects of the default legend:

1. I changed the legend level labels to include a C for category.
2. I changed the the legend title to S.S. category to reflect the official name of the category system, Saffir Simpson.
3. I then changed three aspects of the legend within the theme() function:
   1. the color of the legend background to light blue
   2. the position of the legend to the bottom
   3. the face of the legend title to bold

ex_plot7 <- ggplot(data = storms, aes(x = ts_diameter, y = pressure)) +
  geom_point(aes(color = category), 
             alpha = 0.3,
             size = 1.5) +
  scale_color_manual(values=c("#00b10d", "#ffd800", "#ff9901", "#ff0100", "#be0032",
                    "#79016d", "#7a2fa1"), 
                    labels = c("C-1", "C0", "C1", "C2", "C3", "C4", "C5")) +
  labs(x = "wind diameter", y = "air pressure", title = "Example Plot 7", 
       subtitle = "Plot of air pressure by wind diameter", 
       caption = "Data Source: Dplyr Storms",
       color = "S.S. Category") +
  theme(plot.title = element_text(face = "bold"),
        plot.subtitle = element_text(face = "italic"),
        plot.caption = element_text(size = 6),
        legend.background = element_rect(fill = "light blue"),
        legend.position = "bottom", 
        legend.title = element_text(face = "bold")) 

ex_plot7

8. Reference Lines

Reference lines, smoothers, other regression componenents are really easy to add: Just include an additional geom()!

There are 4 geoms() commonly used for these plot elements:

• geom_vline() for vertical reference lines
• geom_hline() for horizontal reference lines
• geom_abline() for reference lines with a slope and intercept. (note this is usually the go-to function for including a reference regression line)
• geom_smooth() for adding a smoother on top of your data

Example Plot 8

In this example, I will be leveraging information about the Saffir Simpson storm category system used in this plot to add an informative reference line. According to the S.S. system, storm damages become devastating or worse as categories >= 3. This corresponds to a wind speed of 111 mph. So, lets add a reference line at wind diameter (x axis) = 111. We accomplish this with a geom_vline().

ex_plot8 <- ggplot(data = storms, aes(x = ts_diameter, y = pressure)) +
  geom_point(aes(color = category), 
             alpha = 0.3,
             size = 1.5) +
  geom_vline(xintercept = 111, linetype = "dashed") + #linetype = dashed, default is solid
  scale_color_manual(values=c("#00b10d", "#ffd800", "#ff9901", "#ff0100", "#be0032",
                    "#79016d", "#7a2fa1"), 
                    labels = c("C-1", "C0", "C1", "C2", "C3", "C4", "C5")) +
  labs(x = "wind diameter", y = "air pressure", title = "Example Plot 8", 
       subtitle = "Plot of air pressure by wind diameter", 
       caption = "Data Source: Dplyr Storms",
       color = "S.S. Category") +
  theme(plot.title = element_text(face = "bold"),
        plot.subtitle = element_text(face = "italic"),
        plot.caption = element_text(size = 6),
        legend.background = element_rect(fill = "light blue"),
        legend.position = "bottom", 
        legend.title = element_text(face = "bold")) 

ex_plot8

9. Facets

Faceting is ggplot’s way for partitioning a plot into a matrix of panels, where each panel shows a different subset of the data.

2 functions for creating facets: - facet_wrap(): define subsets as the levels of a single grouping variable - facet_grid(): define subsets as the crossing of two grouping variables

Goal: Facilitates comparison among subsets

Visual elements of facets can be changed from within the theme() function using the strip.background() function. Here, you can change the color of the outline of the facet label, the fill of the facet label box, and the type of outline line used.

Example Plot 9

In this example, suppose I want to know how the relationship between wind diameter and air pressure by storm category varies based on the storm status (tropical depression, tropical storm, or hurricane). I can do this using the facet_wrap() function on the status variable, below.

ex_plot9 <- ggplot(data = storms, aes(x = ts_diameter, y = pressure)) +
  geom_point(aes(color = category), 
             alpha = 0.3,
             size = 1.5) +
  geom_vline(xintercept = 111, linetype = "dashed") +
  scale_color_manual(values=c("#00b10d", "#ffd800", "#ff9901", "#ff0100", "#be0032",
                    "#79016d", "#7a2fa1"), 
                    labels = c("C-1", "C0", "C1", "C2", "C3", "C4", "C5")) +
  labs(x = "wind diameter", y = "air pressure", title = "Example Plot 9", 
       subtitle = "Plot of air pressure by wind diameter", 
       caption = "Data Source: Dplyr Storms",
       color = "S.S. Category") +
  theme(plot.title = element_text(face = "bold"),
        plot.subtitle = element_text(face = "italic"),
        plot.caption = element_text(size = 6),
        legend.background = element_rect(fill = "light blue"),
        legend.position = "bottom", 
        legend.title = element_text(face = "bold")) +
  facet_wrap(~ status)

ex_plot9

Reording Facets

We can also re-order the facets. For example, suppose we wanted to reorder the storm statuses to reflect increasing severity (first tropical depression, then tropical storm, last hurricane).

We can do that using the factor(var, levels = c(“level1”, “level2”, … , “leveln”)) convention. See plot clode below.

Example Plot 10

ex_plot10 <- ggplot(data = storms, aes(x = ts_diameter, y = pressure)) +
  geom_point(aes(color = category), 
             alpha = 0.3,
             size = 1.5) +
  geom_vline(xintercept = 111, linetype = "dashed") +
  scale_color_manual(values=c("#00b10d", "#ffd800", "#ff9901", "#ff0100", "#be0032",
                    "#79016d", "#7a2fa1"), 
                    labels = c("C-1", "C0", "C1", "C2", "C3", "C4", "C5")) +
  labs(x = "wind diameter", y = "air pressure", title = "Example Plot 10", 
       subtitle = "Plot of air pressure by wind diameter", 
       caption = "Data Source: Dplyr Storms",
       color = "S.S. Category") +
  theme(plot.title = element_text(face = "bold"),
        plot.subtitle = element_text(face = "italic"),
        plot.caption = element_text(size = 6),
        legend.background = element_rect(fill = "light blue"),
        legend.position = "bottom", 
        legend.title = element_text(face = "bold")) +
  facet_wrap(~ factor(status, levels = c("tropical depression", "tropical storm", "hurricane")))

ex_plot10

10. Themes

ggplot has several build-in themes that you can apply to your visualization. A comprehensive list can be found here, with images of how each theme displays. Examples include:

• theme_gray(): The signature ggplot2 theme with a grey background and white gridlines, designed to put the data forward yet make comparisons easy.
• theme_bw(): The classic dark-on-light ggplot2 theme. May work better for presentations displayed with a projector.
  
• theme_linedraw(): A theme with only black lines of various widths on white backgrounds, reminiscent of a line drawing. Serves a purpose similar theme_bw(). Note that this theme has some very thin lines (<< 1 pt) which some journals may refuse.
• theme_minimal(): A minimalistic theme with no background annotations.
• theme_classic(): A classic-looking theme, with x and y axis lines and no gridlines.

Even more themes can be used from external packages such as ggthemes and ggthmr.

Example Plot 11

Using the ggthemes package, we can apply the theme_bw() theme:

#install.packages("ggthemes") #only need to run once
library(ggthemes)


ex_plot11 <- ggplot(data = storms, aes(x = ts_diameter, y = pressure)) +
  geom_point(aes(color = category), 
             alpha = 0.3,
             size = 1.5) +
  geom_vline(xintercept = 111, linetype = "dashed") +
  scale_color_manual(values=c("#00b10d", "#ffd800", "#ff9901", "#ff0100", "#be0032",
                    "#79016d", "#7a2fa1"), 
                    labels = c("C-1", "C0", "C1", "C2", "C3", "C4", "C5")) +
  labs(x = "wind diameter", y = "air pressure", title = "Example Plot 11", 
       subtitle = "Plot of air pressure by wind diameter", 
       caption = "Data Source: Dplyr Storms",
       color = "S.S. Category") +
  theme_bw() +
  theme(plot.title = element_text(face = "bold"),
        plot.subtitle = element_text(face = "italic"),
        plot.caption = element_text(size = 9),
        legend.background = element_rect(fill = "light blue"),
        legend.position = "bottom", 
        legend.title = element_text(face = "bold"),
        strip.background = element_rect(color = "black", size = 0.5),) +
  facet_wrap(~ factor(status, levels = c("tropical depression", "tropical storm", "hurricane"))) 

ex_plot11

Note: I’ve found that applying the overall theme first (theme_bw) is good practice, because sometimes it has elements that might over-ride the general theme() call.

11. Combining Plots

Sometimes, you want a figure that has multiple plots combined.

The R packages, gridExtra and cowplot can do this!

In this example, we will use cowplot. Cowplot operates on an (X, Y) coordinate plane where X and Y go from 0 to 1. The draw_plot function (seen below) takes on 5 arguments, the plot to be included in the figure, the x and y coordinates for the bottom left corner of that plot, and the width and height for the plot.

Example Plot 12

In this example, ex_plot1's bottom left corner will begin at the origin (x and y are set to 0) and will take up half of the x axis and y axis in width and height, respectively (height and width are set to 0.5).

#install.packages("cowplot") only need to run this once
library(cowplot)

ggdraw() + 
  draw_plot(ex_plot11, x = 0, y = 0.5, width = 1, height = 0.5) +
  draw_plot(ex_plot1, x = 0, y = 0, width = 0.5, height = 0.5) +
  draw_plot(ex_plot4, x = 0.5, y = 0, width = 0.5, height = 0.5)

12. Exporting Plots

Once you have a plot or figure that you’re ready to use, you can export it to multiple formats using one of 3 main methods.

• From within R Studio: export > save as Image or PDF -Advantage: you can specify extension (.jpeg, .png, etc.) and the exact width and height.
• In R code: Use dev system:

#png('~/Desktop/rplot.png', height = 500, width = 500)
ex_plot11
dev.off()

• From within R Studio: zoom plot > drag corner to adjust sizing > right click > save image as
  • Advantage: allows you to play with sizing “live”
  • Disadvantage: unknown dimensions

13. Additional Resources:

• Modern Data Science with R (Baumer, Kaplan, Horton)
• Dr. Ben Baumer’s Graphics with ggplot Github lecture as part of a Smith College course linked here
• ggplot cheat-sheet here